Coronary artery disease is one of the most common cardiovascular diseases. Lipid and calcium deposits in the artery wall weaken the coronary artery and there is increasing evidence that the thickness of the intima behind these lipid and calcium deposits are an indicater for the risk of coronary artery disease. As a result, there is increasing clinical interest in detecting lipid and calcium deposits and measuring intima thickness from cardiac optical coherence tomography (OCT) recordings. Such detection and measurements are now largely done manually. This process requires expertise and is time consuming since typical scans consist of hundreds of images. Automating this process using deep learning provides solution that can be applied quicker and at lower cost, enabling better care for patients with coronary artery disease.
The RTC Deep Learning has developed a web-based application for the automatic segmentation of the lumen, wall, lipid and calcium in cardiac OCT scans. The algorithm can be used on the Grand Challenge platform. All processing is performed on the platform; there is no specialized hardware required to try out the algorithm. Running the algorithm requires an account for Grand Challenge. If you don't have an account yet, you can register on the website; alternatively, you can log in using a Google account. After registering for a new account, or logging in to an existing Grand Challenge account, you can request access to the algorithm.